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Snowflake SnowPro Advanced: Data Scientist Certification Sample Questions:

1. A retail company is using Snowflake to store sales data'. They have a table called 'SALES DATA' with columns: 'SALE ID', 'PRODUCT D', 'SALE DATE', 'QUANTITY' , and 'PRICE'. The data scientist wants to analyze the trend of daily sales over the last year and visualize this trend in Snowsight to present to the business team. Which of the following approaches, using Snowsight and SQL, would be the most efficient and appropriate for visualizing the daily sales trend?

A) Export all the data from the 'SALES DATA' table to a CSV file and use an external tool like Python's Matplotlib or Tableau to create the visualization.
B) Write a SQL query that calculates the daily total sales amount CSUM(QUANTITY PRICEY) for the last year and use Snowsight's charting options to generate a line chart with 'SALE DATE on the x-axis and daily sales amount on the y-axis.
C) Write a SQL query that uses 'DATE TRUNC('day', SALE DATE)' to group sales by day and calculate the total sales (SUM(QUANTITY PRICE)). Use Snowsight's line chart option with the truncated date on the x-axis and total sales on the y-axis, filtering by 'SALE_DATE' within the last year. Furthermore, use moving average with window function to smooth the data.
D) Create a Snowflake view that aggregates the daily sales data, then use Snowsight to visualize the view data as a table without any chart.
E) Use the Snowsight web UI to manually filter the 'SALES_DATX table by 'SALE_DATE for the last year and create a bar chart showing 'SALE_ID count per day.

2. You are building a data science pipeline in Snowflake to predict customer churn. The pipeline involves extracting data, transforming it using Dynamic Tables, training a model using Snowpark ML, and deploying the model for inference. The raw data arrives in a Snowflake stage daily as Parquet files. You want to optimize the pipeline for cost and performance. Which of the following strategies are MOST effective, considering resource utilization and potential data staleness?

A) Use a combination of Dynamic Tables for feature engineering and Snowpark ML for model training and deployment, ensuring proper dependency management and refresh intervals for each Dynamic Table based on data freshness requirements.
B) Implement a series of smaller Dynamic Tables, each responsible for a specific transformation step, with well-defined refresh intervals tailored to the data's volatility and the downstream model's requirements.
C) Load all data into traditional Snowflake tables and use scheduled tasks with stored procedures written in Python to perform the transformations and model training.
D) Use a single, large Dynamic Table to perform all transformations in one step, relying on Snowflake's optimization to handle dependencies and incremental updates.
E) Schedule all data transformations and model training as a single large Snowpark Python script executed by a Snowflake task, ignoring data freshness requirements.

3. You have a Snowflake table 'PRODUCT_PRICES' with columns 'PRODUCT_ID' (INTEGER) and 'PRICE' (VARCHAR). The 'PRICE' column sometimes contains values like '10.50 USD', '20.00 EUR', or 'Invalid Price'. You need to convert the 'PRICE column to a NUMERIC(10,2) data type, removing currency symbols and handling invalid price strings by replacing them with NULL. Considering both data preparation and feature engineering, which combination of Snowpark SQL and Python code snippets achieves this accurately and efficiently, preparing the data for further analysis?

A) Option E
B) Option D
C) Option C
D) Option A
E) Option B

4. A marketing team uses Snowflake to store customer purchase data'. They want to segment customers based on their spending habits using a derived feature called The 'PURCHASES' table has columns 'customer id' (IN T), 'purchase_date' (DATE), and 'purchase_amount' (NUMBER). The team needs a way to handle situations where a customer might have missing months (no purchases in a particular month). They want to impute a 0 spend for those months before calculating the average. Which approach provides the most accurate and robust calculation, especially when considering users with sparse purchase history?

A) Create a view containing all months for each customer, left join with the 'PURCHASES' table, impute 0 for null 'purchase_amounts values, and then calculate the average spend. Requires creating a helper table for all the month.
B) Calculate the average spend only for customers with purchases in every month of the year. Ignore other customers in the analysis.
C) Use a window function to calculate the average spend over a fixed window of the last 3 months, ignoring missing months in the calculation.
D) Calculate the total spend for each customer and divide by the number of months since their first purchase: / DATEDlFF(month, CURRENT DATE()) GROUP BY customer_id'.
E) Calculate the average monthly spend directly from the 'PURCHASES' table without accounting for missing months: 'AVG(purchase_amount) GROUP BY customer_id, date_trunc('month',

5. You are tasked with creating a new feature in a machine learning model for predicting customer lifetime value. You have access to a table called 'CUSTOMER ORDERS which contains order history for each customer. This table contains the following columns: 'CUSTOMER ID', 'ORDER DATE, and 'ORDER AMOUNT. To improve model performance and reduce the impact of outliers, you plan to bin the 'ORDER AMOUNT' column using quantiles. You decide to create 5 bins, effectively creating quintiles. You also want to create a derived feature indicating if the customer's latest order amount falls in the top quintile. Which of the following approaches, or combination of approaches, is most appropriate and efficient for achieving this in Snowflake? (Choose all that apply)

A) Use 'WIDTH_BUCKET function, after finding the boundaries of quantile using 'APPROX_PERCENTILE' or 'PERCENTILE_CONT. Using MAX(ORDER to determine recent amount is in top quantile.
B) Use the window function to create quintiles for 'ORDER AMOUNT and then, in a separate query, check if the latest 'ORDER AMOUNT for each customer falls within the NTILE that represents the top quintile.
C) Calculate the 20th, 40th, 60th, and 80th percentiles of the 'ORDER AMOUNT' using 'APPROX PERCENTILE or 'PERCENTILE CONT and then use a 'CASE statement to assign each order to a quantile bin. Calculate and see if on that particular date is in top quintile.
D) Create a temporary table storing quintile information, then join this table to original table to find the top quintile order amount.
E) Use a Snowflake UDF (User-Defined Function) written in Python or Java to calculate the quantiles and assign each 'ORDER AMOUNT to a bin. Later you can use other statement to check the top quintile amount from result set.

Solutions: